The purpose of the crop and climate modeling group at CIAT is to compare climate scenarios/ stories in order to understand how changes in long-term climate and short-term climate variability affect agricultural productivity and opportunities for crop production in the near-, medium- and long-term. Crop modeling helps estimate agricultural yields as a function of factors such as: weather conditions, soil characteristics, and management practices. On the climate side, we produce seasonal climate forecasts for the upcoming growing season, and then we produce downscaled climate model output for longer-term scenarios. These climate scenarios are then used as input for crop and economic models aimed to measure impacts of climate variability and change on commercial and subsistence crops.

Sharon gives us an insight on what it is that the team does:

“One of our key activities now is to generate seasonal agro-climatic forecasts here in Colombia. This is useful information for farmers. Let’s say, we know that this year the phenomenon of El Niño will take place, we can recommend the farmers to plant less, plant earlier and/or use different varieties. Furthermore, we share the forecast recommendations through agricultural round tables with local experts that know the specific area and can give more precise recommendations.

In the end, it’s still models of course, and we always communicate that very clearly. We show literally what the models say, and explain how to interpret them.

The idea of our team is that we are a collection of different projects, in which each individual has their own area of expertise. This way we are less tied to specific funding for our team and work more integrally across the different research groups within CIAT. For example, our models and expertise are used by the Economic Impact Modelling team in DAPA to generate economic scenarios associated with medium-term climate change in Latin America for a project funded by the Inter-American Development Bank.

We are also developing a new process-based cassava model for incorporation into the DSSAT suite of mechanistic crop models. We are able to do this drawing on the deep plant physiology and agronomy knowledge of cassava that we have here at CIAT.

We also do some work using crop models to simulate future target environments to support crop breeding efforts. The idea is to identify what types of environments growers will most typically face under future climate conditions in the say 30-year time-frame (which is the lifecycle time for crop breeding).

Photocredit: Neil Palmer (CIAT)

In the longer term, we need to test and (re)evaluate our models, especially within the context of the changing climate with higher temperatures and more variable rainfall patterns. For example, with crop models, many of the descriptions of key physiological processes have not been updated to reflect new understanding and new datasets collected in recent years, and many processes important for simulating climate change, e.g. extreme heat at flowering, are not included at all in the models. ”